{"total":57,"rows":[{"id":"2111.05464","title":"Are Transformers More Robust Than CNNs?","created":"2021-11-10","cited_by_count":187,"area":null},{"id":"2402.01739","title":"OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models","created":"2024-01-29","cited_by_count":187,"area":"moe"},{"id":"2403.17695","title":"PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition","created":"2024-03-26","cited_by_count":141,"area":"ssm"},{"id":"2305.13230","title":"To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis","created":"2023-05-22","cited_by_count":92,"area":"moe"},{"id":"2303.06318","title":"A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training","created":"2023-03-11","cited_by_count":47,"area":"moe"},{"id":"2206.03452","title":"Can CNNs Be More Robust Than Transformers?","created":"2022-06-07","cited_by_count":38,"area":null},{"id":"2310.18859","title":"SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models","created":"2023-10-29","cited_by_count":38,"area":"moe"},{"id":"2303.06182","title":"Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference","created":"2023-03-10","cited_by_count":37,"area":"moe"},{"id":"2203.09125","title":"Are Vision Transformers Robust to Spurious Correlations?","created":"2022-03-17","cited_by_count":36,"area":null},{"id":"2404.05019","title":"Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts","created":"2024-04-07","cited_by_count":32,"area":"moe"},{"id":"2308.10110","title":"Robust Mixture-of-Expert Training for Convolutional Neural Networks","created":"2023-08-19","cited_by_count":30,"area":"moe"},{"id":"2210.10253","title":"On the Adversarial Robustness of Mixture of Experts","created":"2022-10-19","cited_by_count":28,"area":"moe"},{"id":"2201.10890","title":"One Student Knows All Experts Know: From Sparse to Dense","created":"2022-01-26","cited_by_count":25,"area":"moe"},{"id":"2407.19985","title":"Mixture of Nested Experts: Adaptive Processing of Visual Tokens","created":"2024-07-29","cited_by_count":25,"area":"moe"},{"id":"2410.07265","title":"A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models","created":"2024-10-08","cited_by_count":23,"area":null},{"id":"2408.06793","title":"Layerwise Recurrent Router for Mixture-of-Experts","created":"2024-08-13","cited_by_count":21,"area":"moe"},{"id":"2110.03360","title":"Sparse MoEs meet Efficient Ensembles","created":"2021-10-07","cited_by_count":19,"area":null},{"id":"2109.02008","title":"Cross-token Modeling with Conditional Computation","created":"2021-09-05","cited_by_count":17,"area":"moe"},{"id":"2312.17276","title":"PanGu-$\\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation","created":"2023-12-27","cited_by_count":17,"area":null},{"id":"2305.02177","title":"Transforming Visual Scene Graphs to Image Captions","created":"2023-05-03","cited_by_count":15,"area":"moe"},{"id":"2403.01197","title":"DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modeling","created":"2024-03-02","cited_by_count":15,"area":"alignment"},{"id":"2204.10598","title":"Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability","created":"2022-04-22","cited_by_count":14,"area":"moe"},{"id":"2207.09094","title":"MoEC: Mixture of Expert Clusters","created":"2022-07-19","cited_by_count":14,"area":"moe"},{"id":"2409.19660","title":"All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation","created":"2024-09-29","cited_by_count":14,"area":null},{"id":"2308.06093","title":"Experts Weights Averaging: A New General Training Scheme for Vision Transformers","created":"2023-08-11","cited_by_count":12,"area":"moe"},{"id":"2309.00655","title":"RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion","created":"2023-09-01","cited_by_count":11,"area":null},{"id":"2111.00856","title":"Large-Scale Deep Learning Optimizations: A Comprehensive Survey","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2510.17483","title":"ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts","created":"2025-10-20","cited_by_count":10,"area":"moe"},{"id":"2210.00640","title":"Wide Attention Is The Way Forward For Transformers?","created":"2022-10-02","cited_by_count":8,"area":null},{"id":"2502.06832","title":"Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach","created":"2025-02-05","cited_by_count":8,"area":"moe"},{"id":"2108.03645","title":"Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters","created":"2021-08-08","cited_by_count":7,"area":null},{"id":"2505.03310","title":"3D Gaussian Splatting Data Compression with Mixture of Priors","created":"2025-05-06","cited_by_count":6,"area":"moe"},{"id":"2207.03481","title":"Training Transformers Together","created":"2022-07-07","cited_by_count":5,"area":null},{"id":"2405.00568","title":"Powering In-Database Dynamic Model Slicing for Structured Data Analytics","created":"2024-05-01","cited_by_count":5,"area":"moe"},{"id":"2205.10505","title":"A Study on Transformer Configuration and Training Objective","created":"2022-05-21","cited_by_count":4,"area":null},{"id":"2408.04307","title":"MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training","created":"2024-08-08","cited_by_count":4,"area":"moe"},{"id":"2502.05602","title":"UbiMoE: A Ubiquitous Mixture-of-Experts Vision Transformer Accelerator With Hybrid Computation Pattern on FPGA","created":"2025-02-08","cited_by_count":4,"area":"moe"},{"id":"2209.08326","title":"Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition","created":"2022-09-17","cited_by_count":3,"area":"moe"},{"id":"2302.00247","title":"TAPAS: Fast and Automatic Derivation of Tensor Parallel Strategies for Large Neural Networks","created":"2023-02-01","cited_by_count":3,"area":null},{"id":"2303.12892","title":"Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset","created":"2023-03-22","cited_by_count":3,"area":"moe"},{"id":"2505.19427","title":"WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference","created":"2025-05-26","cited_by_count":3,"area":"moe"},{"id":"2301.10750","title":"Out of Distribution Performance of State of Art Vision Model","created":"2023-01-25","cited_by_count":2,"area":null},{"id":"2306.00103","title":"ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2503.23362","title":"Mixture of Routers","created":"2025-03-30","cited_by_count":2,"area":"alignment"},{"id":"2508.08825","title":"Wavelet Mixture of Experts for Time Series Forecasting","created":"2025-08-12","cited_by_count":2,"area":"moe"},{"id":"2407.10157","title":"SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation","created":"2024-07-14","cited_by_count":1,"area":null},{"id":"2410.15732","title":"ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts","created":"2024-10-21","cited_by_count":1,"area":"moe"},{"id":"2604.13761","title":"Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation","created":"2026-04-15","cited_by_count":1,"area":"moe"},{"id":"2306.12100","title":"Efficient ResNets: Residual Network Design","created":"2023-06-21","cited_by_count":0,"area":null},{"id":"2310.12818","title":"Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2506.08496","title":"CoQMoE: Co-Designed Quantization and Computation Orchestration for Mixture-of-Experts Vision Transformer on FPGA","created":"2025-06-10","cited_by_count":0,"area":"moe"},{"id":"2507.20180","title":"MoCTEFuse: Illumination-Gated Mixture of Chiral Transformer Experts for Multi-Level Infrared and Visible Image Fusion","created":"2025-07-27","cited_by_count":0,"area":null},{"id":"2509.10530","title":"Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts","created":"2025-09-05","cited_by_count":0,"area":"moe"},{"id":"2509.20073","title":"SHMoAReg: Spark Deformable Image Registration via Spatial Heterogeneous Mixture of Experts and Attention Heads","created":"2025-09-24","cited_by_count":0,"area":"moe"},{"id":"2512.19731","title":"Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2605.13161","title":"A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning","created":"2026-05-13","cited_by_count":0,"area":"mllm"},{"id":"2610.04198","title":"ALoDLM: Adaptively Looped Diffusion Language Models","created":"2026-10-03","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}